Existing work on controlled text generation (CTG) assumes a control interface
of categorical attributes. In this work, we propose a natural language (NL)
interface, where we craft a PCFG to embed the control attributes into natural
language commands, and propose variants of existing CTG models that take
commands as input. In our experiments, we design tailored setups to test
model's generalization abilities. We find our PCFG-based command generation
approach is effective for handling unseen commands compared to fix-set
templates; our proposed NL models can effectively generalize to unseen
attributes, a new ability enabled by the NL interface, as well as unseen
attribute combinations. Interestingly, we discover that the simple conditional
generation approach, enhanced with our proposed NL interface, is a strong
baseline in those challenging settings.

本论文提出了一种将控制属性嵌入自然语言命令中的基于 PCFG 的生成命令方法，并提出了将命令作为输入的 CTG 模型变体，在实验中发现，这种方法不仅可以有效处理未见过的命令，而且可以有效泛化到未见过的属性及其组合，并通过简单条件生成方法强化了该方法，形成了一个强有力的基线模型。